Evaluation Standards of Intelligent Technology based on Financial Alternative Data

Abstract

After the visions of Industry 5.0 and Society 5.0 were presented, a proliferation of artificial intelligence technologies have been applied to the financial field because AI develops fast, especially intelligent analysis methods for alternative financial data. However, the organic integration of the financial industry and the Internet of Things lacks relevant standards, and there is no appropriate work summary to coordinate the formulation of these standards. This work aims to effectively improve the reliability of information acquisition and the accuracy of data processing in the financial industry. In addition, this work also investigates papers and standards related to financial intelligence technology in recent years and statistically analyzes the evaluation indicators of AI research papers. Then, a standard evaluation framework is proposed for financial intelligence technology, which is evaluated for performance verification. The comparative experiments demonstrate that the prediction accuracy of the financial intelligence standard model reaches 95.44%, and the prediction error is substantially smaller than that of the other model algorithms. The financial intelligence standard model can make accurate predictions on alternative financial data and has high reliability and validity. The model can provide an experimental reference for intellectual development in the financial field and enable participants to improve work efficiency and standards throughout the process of developing intelligent financial technology.